Analytical SLAM Without Linearization
This addresses the issue of errors from linearization in SLAM for robotics and autonomous systems, offering a more robust solution, though it appears incremental as it builds on existing Kalman filtering approaches.
The paper tackles the SLAM problem by avoiding linearized approximations, using a linear time-varying Kalman observer with virtual synthetic measurements to achieve guaranteed convergence rates in full nonlinear contexts, as demonstrated in 2D and 3D simulations.
This paper solves the classical problem of simultaneous localization and mapping (SLAM) in a fashion which avoids linearized approximations altogether. Based on creating virtual synthetic measurements, the algorithm uses a linear time- varying (LTV) Kalman observer, bypassing errors and approximations brought by the linearization process in traditional extended Kalman filtering (EKF) SLAM. Convergence rates of the algorithm are established using contraction analysis. Different combinations of sensor information can be exploited, such as bearing measurements, range measurements, optical flow, or time-to-contact. As illustrated in simulations, the proposed algorithm can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context.